JP6727097B2 - Information processing apparatus, information processing method, and program - Google Patents

Description

The present invention relates to an information processing device, an information processing method, and a program.

BACKGROUND ART Conventionally, there is known a crawler that collects data (documents, images, etc.) from the web and automatically converts the collected data into a database (see Patent Document 1). Crawlers follow the links in web pages to collect data from web pages at various IP addresses. The data collected by the crawler is stored in the web information database.

On the other hand, when the search engine receives the search word input by the user, the search engine acquires the information of the web page related to the received search word from the web information database. Further, the search engine outputs a list of the acquired web page information as a search result.

JP 2012-69171 A

In order to improve user satisfaction, a search engine may output an image associated with a search word input by a user together with a search result. For this purpose, it is necessary to prepare in advance a database that associates the images collected from the web with the entities indicating the words related to the images.

However, the crawler disclosed in Patent Document 1 could not automatically identify the entity related to the image collected from the web. Therefore, the crawler disclosed in Patent Document 1 cannot automatically generate a database in which images collected from the web are associated with entities related to the images.

The present invention has been made in consideration of such circumstances, and an information processing apparatus and information capable of automatically generating a database in which images collected from the web and entities related to the images are associated with each other. One of the purposes is to provide a processing method and a program.

According to an aspect of the present invention, a collection unit that collects page data including image data and text data, and an entity related to the image data included in the page data is extracted from the text data included in the page data. An entity extraction unit, a feature extraction unit that extracts a predetermined feature that can be a data collection target from the image data included in the page data, and if the predetermined feature is extracted by the feature extraction unit, the An information processing device, comprising: a generation unit that generates knowledge data associated with the location information of the image data.

According to one aspect of the present invention, it is possible to automatically generate a database associating images collected from the web with entities related to the images.

It is a figure which shows the usage environment of the knowledge data server 100 which concerns on 1st Embodiment. It is a figure which shows an example of the search result page displayed on the display part 210 of the terminal device 200 which concerns on 1st Embodiment. It is a block diagram which shows the detailed structure of the knowledge data production|generation part 120 which concerns on 1st Embodiment. It is a figure which shows an example of the category data D1 which concerns on 1st Embodiment. It is a figure which shows an example of the knowledge data D2 which concerns on 1st Embodiment. It is a figure which shows an example of the candidate of the image linked|related with the entity E which concerns on 1st Embodiment. It is a figure which shows an example of the image linked|related with the entity E which concerns on 1st Embodiment. It is a flow chart which shows knowledge data generation processing concerning a 1st embodiment. It is a figure which shows an example of the candidate of the image linked|related with the entity E which concerns on 2nd Embodiment. It is a figure which shows an example of distribution of the vector data which concerns on 2nd Embodiment. It is a figure which shows an example of the hardware constitutions of the knowledge data server 100 which concerns on embodiment.

Embodiments of an information processing device, an information processing method, and a program will be described below with reference to the drawings. In the present embodiment, a knowledge data server will be described as an example of an information processing device. The knowledge data server is, for example, a server that collects page data including image data and text data and generates knowledge data based on the collected data. Knowledge data is data in which an entity and the location information of image data related to this entity are associated with each other. An entity is data that indicates words related to image data collected from the web. The location information is, for example, a URL (Uniform Resource Locator) for specifying a position on the web. Hereinafter, embodiments will be described in detail.

<1. First Embodiment>
<1-1. Knowledge Data Server Usage Environment>
FIG. 1 is a diagram showing a usage environment of the knowledge data server 100 according to the first embodiment. The knowledge data server 100 includes a collection unit 110, a knowledge data generation unit 120, a first storage unit 130, a second storage unit 140, and a location information acquisition unit 150.

The collection unit 110, the knowledge data generation unit 120, and the location information acquisition unit 150 may be realized by, for example, a processor of the knowledge data server 100 executing a program, an LSI (Large Scale Integration), an ASIC (Application). It may be realized by hardware such as a specific integrated circuit) or FPGA (Field-Programmable Gate Array), or may be realized by cooperation between software and hardware.

The first storage unit 130 and the second storage unit 140 are, for example, a RAM (Random Access Memory), a ROM (Read Only Memory), a HDD (Hard Disk Drive), a flash memory, or a hybrid type in which a plurality of these are combined. It is realized by a storage device or the like. Further, part or all of the first storage unit 130 and the second storage unit 140 may be an external device accessible by the knowledge data server 100, such as a NAS (Network Attached Storage) or an external storage server.

The collection unit 110 is a crawler that collects page data including image data and HTML (HyperText Markup Language) data from the web W. Here, the page data is data in page units that are on the web W and can be browsed by a browser. The page data is not limited to the browser and may be data reproduced by an application program. The web W means the World Wide Web, and is a system that uses an HTML document or the like that is standardly used on the Internet or an intranet. The HTML data is text data indicating an HTML source.

The collection unit 110 acquires the URL (location information) of the link destination from the HTML data included in the collected page data, accesses the acquired URL, and further collects the page data. By repeating this processing, the collection unit 110 can widen the IP address of the target for collecting the page data. The collection unit 110 outputs the collected page data to the knowledge data generation unit 120.

The knowledge data generation unit 120 extracts the entity related to the image data included in the page data from the HTML data included in the page data input from the collection unit 110. An entity is information indicating a word related to image data. Further, the knowledge data generation unit 120 generates knowledge data in which the URL of the image data is associated with the extracted entity, and stores the generated knowledge data in the second storage unit 140. As a result, the knowledge data generation unit 120 can automatically generate knowledge data using the page data collected by the collection unit 110.

Although details will be described later, the knowledge data generation unit 120 reads out category data corresponding to the extracted entity from the first storage unit 130, and determines whether to generate knowledge data based on the read out category data. Further, the knowledge data generation unit 120 determines the priority order of IP addresses for collecting page data based on the number of entities for each IP address. Then, the knowledge data generation unit 120 outputs feedback data indicating the determined priority order to the collection unit 110.

The collection unit 110 collects page data from the web W according to the priority order indicated by the feedback data input from the knowledge data generation unit 120. As a result, page data is preferentially collected from IP addresses including many entities, so that the entities can be efficiently extracted.

On the other hand, the terminal device 200 and the web server 300 are communicatively connected via a network, and the knowledge data server 100 and the web server 300 are communicatively connected via a network. The terminal device 200 is a device used by a user, and is, for example, a tablet computer, a notebook computer, a desktop computer, or a smartphone. The web server 300 is a server that generates search result page information for displaying a search result using a search engine.

The user uses the terminal device 200 to input a search query on the search page displayed on the display unit of the terminal device 200. The search query is one search word or a combination of a plurality of search words. The terminal device 200 transmits the search query input by the user to the web server 300.

The web server 300 uses the search engine to generate search result page data based on the search query received from the terminal device 200. At this time, the web server 300 includes not only the search result but also the image related to the entity included in the search query in the search result page data.

<1-2. Search result page>
FIG. 2 is a diagram showing an example of a search result page displayed on the display unit 210 of the terminal device 200 according to the first embodiment. As shown in FIG. 2, the display unit 210 displays a search result page including a search query input area 211, a knowledge panel 220, and a search result 230. The knowledge panel 220 includes an entity 221 included in the search query, an image 222 related to the entity 221, and information 223 related to the entity 221.

In the example shown in FIG. 2, “actress A” is input as the search query, and the knowledge panel 220 displays images and various information related to “actress A”. As described above, by displaying not only the search result 230 but also the knowledge panel 220 on the display unit 210, it is possible to improve user satisfaction with the search result.

In order to display the knowledge panel 220 on the search result page, the web server 300 needs to obtain images related to the entities included in the search query. Therefore, the web server 300 sends the entities included in the search query to the knowledge data server 100.

As described above, the second storage unit 140 of the knowledge data server 100 stores the knowledge data in which the URL of the image data is associated with the entity. The location information acquisition unit 150 refers to the knowledge data stored in the second storage unit 140 and acquires the URL of the image data corresponding to the entity received from the web server 300. After that, the location information acquisition unit 150 transmits the URL of the acquired image data to the web server 300.

As described above, when the location information acquisition unit 150 receives the entity from the web server 300, the location information acquisition unit 150 acquires the URL of the image data associated with the received entity from the second storage unit 140, and acquires the URL of the acquired image data from the web. It is transmitted to the server 300. Accordingly, the web server 300 can acquire the URL of the image related to the entity included in the search query.

The web server 300 uses the URL of the image data received from the location information acquisition unit 150 and the search result generated using the search engine to generate search result page data. The web server 300 transmits the generated search result page data to the terminal device 200. When the terminal device 200 receives the search result page data from the web server 300, the terminal device 200 displays the search result page (FIG. 2) on the display unit 210 using the received search result page data.

<1-3. Process of Knowledge Data Generation Unit>
FIG. 3 is a block diagram showing a detailed configuration of the knowledge data generation unit 120 according to the first embodiment. Hereinafter, a specific process of the knowledge data generation unit 120 will be described with reference to FIG. As described above, page data including image data and HTML data is input to the knowledge data generation unit 120. The knowledge data generation unit 120 includes an entity extraction unit 121, a feature extraction unit 122, a generation unit 123, and a priority order determination unit 124.

The entity extraction unit 121 extracts the entity related to the image data from the HTML data included in the page data. For example, the entity extracting unit 121 acquires the text around the position where the image data is embedded in the page data from the HTML data, and extracts the entity from the acquired text. The entity extraction unit 121 outputs the extracted entity and HTML data to the generation unit 123.

The entity extracting unit 121 extracts the entity from the text around the position where the image data is embedded, but the present invention is not limited to this. For example, the entity extracting unit 121 may extract the entity from the data described in the HTML data tag.

Specifically, the entity extracting unit 121 may extract the entity from the data described in the alt attribute of the img tag. An example of the img tag is shown below.
<img src=“actress-a.jpg” alt=“Actress A who has been selected as the heroine of the movie” />
In this case, the entity extracting unit 121 extracts “actress A” from the data described in the alt attribute as an entity related to the image with the file name “actress-a.jpg”.

Further, the entity extracting unit 121 may extract the entity from the data described in the meta tag of OGP (Open Graph Protocol). An example of the OGP meta tag is shown below.
<meta property=“og:title” content=“Actress A who has been selected as the heroine of the movie” />
<meta property=“og:image” content=“actress-a.jpg” />
In this case, the entity extraction unit 121 uses the meta tag with the property “og:title” as the entity related to the image with the file name “actress-a.jpg” described in the meta tag with the property “og:image”. "Actress A" is extracted from the data described in.

The feature extraction unit 122 extracts features from the image data included in the page data. Then, the feature extraction unit 122 outputs the feature data indicating the extracted features to the generation unit 123. For example, when the image data includes a person image, the feature extraction unit 122 extracts a human face from the image data using a face detection algorithm such as the Viola-Jones method. Then, the feature extraction unit 122 outputs data indicating that a human face has been extracted and data indicating the number of extracted faces to the generation unit 123 as feature data.

FIG. 4 is a diagram showing an example of the category data D1 according to the first embodiment. The category data D1 is stored in the first storage unit 130. As shown in FIG. 4, the category data D1 is data in which an entity and a category are associated with each other. For example, if entity 1 is a person's name, then entity 1 is associated with the category "person." If entity 2 is the title of a movie, then entity 2 is associated with the category "movie". If entity n is a drama title, entity n is associated with the category "drama". The category data D1 is not limited to the category related to the type of entity, and may include information related to the entity used by the generation unit 123.

The generation unit 123 refers to the category data D1 stored in the first storage unit 130 and acquires the category associated with the entity extracted by the entity extraction unit 121. Further, the generation unit 123 acquires the URL of the image data from the HTML data tag (img tag or meta tag).

When the feature extraction unit 122 extracts the feature related to the category acquired from the first storage unit 130, the generation unit 123 associates the entity extracted by the entity extraction unit 121 with the URL of the image data to generate the knowledge. Data D2 is generated. The generation unit 123 stores the generated knowledge data D2 in the second storage unit 140.

FIG. 5 is a diagram showing an example of the knowledge data D2 according to the first embodiment. As shown in FIG. 5, the knowledge data D2 is data in which the entity and the URL of the image data are associated with each other. In the knowledge data D2, one entity may be associated with a plurality of URLs of image data, or one image data URL may be associated with a plurality of entities. The generation unit 123 accumulates the generated knowledge data D2 in the second storage unit every time the knowledge data D2 is generated.

For example, when the entity extracted by the entity extraction unit 121 is a person's name, and the feature extraction unit 122 extracts only one human face from the image data, the generation unit 123 associates the entity with the feature extraction unit 122. Judge that the feature is extracted. In this case, the generation unit 123 generates the knowledge data D2 by associating the entity extracted by the entity extraction unit 121 with the URL of the image data described in the HTML data tag (img tag or meta tag).

The generating unit 123 generates the knowledge data D2 by associating the entity with the URL of the image data, but the invention is not limited to this. For example, the knowledge data generation unit 120 may store the image data included in the page data collected by the collection unit 110 in the second storage unit 140. In this case, the generation unit 123 may generate the knowledge data D2 by associating the entity with the address of the image data in the second storage unit 140.

FIG. 6 is a diagram showing an example of image candidates associated with the entity E according to the first embodiment. As shown in FIG. 6, image IMG1 is an image of actress A, image IMG2 is an image of actress A and another woman, and image IMG3 is an image of a car owned by actress A. In this way, various types of images are acquired as candidate images associated with the entity E, depending on the text included in the HTML data.

In the example shown in FIG. 6, since the entity E is a person's name, the generation unit 123 associates the image in which only one human face is extracted by the feature extraction unit 122 with the entity E. Specifically, the generation unit 123 associates the URL of the image IMG1 with the entity E. Further, since two human faces are detected from IMG2, the generation unit 123 does not associate the URL of the image IMG2 with the entity E. Further, since no human face is detected from IMG3, the generation unit 123 does not associate the URL of the image IMG3 with the entity E.

FIG. 7 is a diagram showing an example of an image associated with the entity E according to the first embodiment. As shown in FIG. 7, the entity E is associated with a plurality of images IMG4 to IMG6. In this case, it is necessary to determine which image should be preferentially displayed on the knowledge panel 220 (see FIG. 2) of the search result page.

Therefore, the generation unit 123 may rank the plurality of images IMG4 to IMG6 based on the resolution, type, aspect ratio of the images, the number of clicks by the user, a combination thereof, or the like. Further, the image with the highest rank may be displayed on the knowledge panel 220 (see FIG. 2) of the search result page.

On the other hand, the generation unit 123 calculates the number of entities extracted by the entity extraction unit 121 for each IP address of image data. The generation unit 123 outputs the calculated number of entities for each IP address to the priority order determination unit 124. The priority order determination unit 124 determines the priority order of the IP addresses for which the collection unit 110 collects data, based on the number of entities for each IP address input from the generation unit 123.

For example, the priority order determination unit 124 increases the priority order for an IP address with a larger number of entities extracted by the entity extraction unit 121. The priority order determination unit 124 outputs feedback data indicating the determined priority order to the collection unit 110.

The collection unit 110 collects page data from the web W according to the priority order indicated by the feedback data input from the priority order determination unit 124. Specifically, the collection unit 110 collects page data in order from the IP address with the highest priority. As a result, page data is preferentially collected from IP addresses including many entities, so that the entities can be efficiently extracted.

<1-4. Flowchart of knowledge data generation process>
FIG. 8 is a flowchart showing the knowledge data generation process according to the first embodiment. The process according to this flowchart is executed by the knowledge data server 100.

First, the collection unit 110 collects page data from the web W (S10). The page data includes HTML data and image data. The entity extracting unit 121 extracts the entity related to the image data from the HTML data included in the page data collected by the collecting unit 110 (S11).

The feature extraction unit 122 extracts a feature (for example, a human face) from the image data (S12). The generation unit 123 determines whether the features corresponding to the entities extracted by the entity extraction unit 121 have been extracted (S13). When the generation unit 123 determines that the feature corresponding to the entity extracted by the entity extraction unit 121 is not extracted, the process proceeds to S15 described below.

On the other hand, when the generation unit 123 determines that the feature corresponding to the entity extracted by the entity extraction unit 121 is extracted, the generation unit 123 generates knowledge data in which the URL of the image data is associated with the entity extracted by the entity extraction unit 121. Yes (S14).

After that, the generation unit 123 determines whether the generation of the knowledge data has been completed for all the IP addresses for which the page data has been collected by the collection unit 110 (S15). When the generation unit 123 determines that the generation of knowledge data has not been completed for all IP addresses, the process returns to S11 described above.

On the other hand, when the generation unit 123 determines that the generation of knowledge data has been completed for all IP addresses, the generation unit 123 calculates the number of entities extracted by the entity extraction unit 121 for each IP address of image data. After that, the priority order determination unit 124 determines the priority order of the IP addresses for which the collection unit 110 collects data based on the number of entities for each IP address calculated by the generation unit 123 (S16), and the process according to this flowchart To finish.

As described above, the collection unit 110 collects page data including image data and HTML data. The entity extraction unit 121 extracts the entity related to the image data included in the page data from the HTML data included in the page data. The feature extraction unit 122 extracts a predetermined feature that can be a data collection target from the image data included in the page data. When the predetermined feature is extracted by the feature extraction unit 122, the generation unit 123 generates the knowledge data D2 in which the URL of the image data is associated with the entity. This allows a database to be automatically generated that associates images collected from the web with the entities associated with this image.

Further, since the generation unit 123 generates the knowledge data D2 based on the feature (for example, a human face) extracted by the feature extraction unit 122, the generation unit 123 can generate the knowledge data D2 more than when the feature extraction unit 122 does not extract the feature from the image data. Knowledge data D2 can be generated with high accuracy.

In addition, the priority order determination unit 124 determines the priority order of the IP addresses for which the collection unit 110 collects data, based on the number of entities for each IP address to which the image data belongs extracted by the entity extraction unit 121. As a result, page data is preferentially collected from IP addresses including many entities, so that the entities can be efficiently extracted.

The priority order determination unit 124 determines the priority order of the IP addresses based on only the number of entities for each IP address to which the image data belongs, but the present invention is not limited to this. For example, the priority order determination unit 124 may determine the priority order in consideration of the reliability of the page in addition to the number of entities related to the image data.

<2. Second Embodiment>
<2-1. Knowledge Data Server Usage Environment>
In the first embodiment, the feature extraction unit 122 extracts features by performing image processing (for example, face detection processing such as the Viola-Jones method) on the image data input from the collection unit 110. I decided. On the other hand, in the second embodiment, the feature extraction unit 122 converts the image data input from the collection unit 110 into a feature amount expression. In this embodiment, vector data is used as an example of the feature amount expression. By using the feature amount expression converted from the image data, it is possible to prevent, for example, an entity of a person's name from being associated with an image of another person with the same surname. Hereinafter, the second embodiment will be described in detail.

FIG. 9 is a diagram illustrating an example of image candidates associated with the entity E according to the second embodiment. As shown in FIG. 9, the image IMG7 is an image of the actress A, the image IMG8 is also an image of the actress A, and the image IMG9 is an image of Mr. B who has the same surname and the same name as the actress A. In this way, the image of Mr. B who is different from the actress A may be acquired as a candidate image associated with the entity E indicating the actress A. Therefore, it is necessary not to associate the image IMG9 of Mr. B with the entity E indicating the actress A.

Therefore, the feature extraction unit 122 converts the image data input from the collection unit 110 into multidimensional vector data (feature amount expression) using a converter such as Auto-Encoder. The feature extraction unit 122 outputs the converted vector data to the generation unit 123.

FIG. 10 is a diagram showing an example of the distribution of vector data according to the second embodiment. Here, for ease of explanation, an example in which each image data is converted into two-dimensional vector data (x, y) will be described. In FIG. 10, the horizontal axis represents the x value of the vector data, and the vertical axis represents the y value of the vector data.

The generation unit 123 classifies the vector data input from the feature extraction unit 122 into a plurality of clusters C1 or C2. In the example shown in FIG. 10, the vector data classified into the cluster C1 is larger than the vector data classified into the cluster C2. Since actress A is more famous than Mr. B, it is considered that actress A has more images on Mr. B than Mr. B. Therefore, the vector data belonging to the cluster C1 is considered to be the vector data corresponding to the image data of the actress A.

Therefore, the generation unit 123 generates the knowledge data D2 by associating the URL of the image data corresponding to the vector data belonging to the cluster C1 having the largest number of data with the entity E. On the other hand, the generation unit 123 does not associate the URL of the image data corresponding to the vector data belonging to the cluster C2 with the entity E.

As described above, the feature extraction unit 122 converts the image data included in the page data into vector data. The generation unit 123 classifies the vector data converted by the feature extraction unit 122 into a plurality of clusters C1 or C2. The generation unit 123 generates the knowledge data D2 by associating the URL of the image data corresponding to the vector data belonging to the cluster having the largest number of data with the entity E. As a result, the URL of the image data having a strong association with the entity E can be associated with the image data. For example, according to the present embodiment, it is possible to prevent an entity of a person's name from being associated with an image of another person with the same surname.

Further, since the generation unit 123 generates the knowledge data D2 based on the feature amount expression (vector data) generated by the feature extraction unit 122, it is more than in the case where the feature extraction unit 122 does not generate the feature amount expression (vector data). Also, the knowledge data D2 can be generated with high accuracy.

Note that the generation unit 123 associates the URL of the image data corresponding to the vector data belonging to the cluster C1 having the largest number of data with the entity E, but the present invention is not limited to this. For example, the generation unit 123 may extract a word included in the HTML data for each vector data belonging to each cluster, and determine whether to associate the URL of the image data with the entity E based on the extracted word. For example, when many words such as “actress” and “movie” are extracted from the HMTL data corresponding to the vector data belonging to the cluster C1, the generation unit 123 determines that the vector data belonging to the cluster C1 becomes the image data of the actress A. The URL of the image data corresponding to the vector data belonging to the cluster C1 may be determined to be the corresponding vector data and associated with the entity E.

Further, in the present embodiment, the feature extraction unit 122 converts the image data into vector data, but may convert into other feature amount expressions.

<3. Hardware configuration>
FIG. 11 is a diagram showing an example of the hardware configuration of the knowledge data server 100 according to the embodiment. In the knowledge data server 100, for example, a CPU 160, a RAM 161, a ROM 162, a secondary storage device 163 such as a flash memory or an HDD, a NIC (Network Interface Card) 164, a drive device 165, a keyboard 166, and a mouse 167 are used as an internal bus or a dedicated one. It is configured to be connected to each other by a communication line. A portable storage medium such as an optical disk is attached to the drive device 165. A program stored in a secondary storage device 163 or a portable storage medium mounted in the drive device 165 is expanded in the RAM 161 by a DMA (Direct Memory Access) controller (not shown) or the like, and executed by the CPU 160. The functional unit of the knowledge data server 100 is realized.

As described above, the knowledge data server 100 of the embodiment includes the collection unit 110, the entity extraction unit 121, the feature extraction unit 122, and the generation unit 123. The collection unit 110 collects page data including image data and HTML data. The entity extraction unit 121 extracts the entity related to the image data included in the page data from the HTML data included in the page data. The feature extraction unit 122 extracts a predetermined feature that can be a data collection target from the image data included in the page data. When the predetermined feature is extracted by the feature extraction unit 122, the generation unit 123 generates the knowledge data D2 in which the URL of the image data is associated with the entity. This allows a database to be automatically generated that associates images collected from the web with the entities associated with this image.

Moreover, the knowledge data server 100 of the embodiment further includes a priority order determination unit 124. The priority order determination unit 124 determines the priority order of the IP addresses for which the collection unit 110 collects data, based on the number of entities for each IP address to which the image data belongs extracted by the entity extraction unit 121. As a result, page data is preferentially collected from IP addresses including many entities, so that the entities can be efficiently extracted.

As described above, the embodiments for carrying out the present invention have been described using the embodiments, but the present invention is not limited to such embodiments, and various modifications and substitutions are made within the scope not departing from the gist of the present invention. Can be added.

100... Knowledge data server 110... Collection unit (crawler)
120... Knowledge data generation unit 121... Entity extraction unit 122... Feature extraction unit 123... Generation unit 124... Priority determination unit 130... First storage unit 140... Second storage unit 150... Location information acquisition unit 200... Terminal device 300... Web server

Claims (14)

A collection unit that collects page data including image data and text data,
From the text data included in the page data, an entity extraction unit that extracts an entity related to the image data included in the page data,
From the image data included in the page data, a feature extraction unit that extracts a predetermined feature that can be a data collection target,
When the predetermined feature is extracted by the feature extraction unit, a generation unit that generates knowledge data in which the location information of the image data is associated with the entity,
A priority order determination unit that determines the priority order of the IP addresses for which the collection unit collects data, based on the number of the entities for each IP address to which the image data belongs extracted by the entity extraction unit;
An information processing apparatus including. A storage unit for storing the knowledge data generated by the generation unit,
The information processing apparatus according to claim 1, further comprising: a location information acquisition unit that acquires location information of the image data with reference to the knowledge data stored in the storage unit. When the location information acquisition unit receives an entity from an external device, the location information of the received image data associated with the entity is acquired from the storage unit, and the location information of the acquired image data is transferred to the external device. The information processing apparatus according to claim 2, which transmits. The said entity extraction part acquires the text around the position where the said image data was embedded in the said page data from the said text data, and extracts the said entity from the acquired said text. The information processing device according to 1. The text data is HTML data indicating an HTML source,
The information processing apparatus according to claim 1, wherein the entity extraction unit extracts the entity from data described in a tag of the HTML data. The information processing according to claim 5, wherein the generation unit generates the knowledge data by associating the location information of the image data described in the HTML data tag with the entity extracted by the entity extraction unit. apparatus. If the entity extracted by the entity extraction unit is a person's name and only one human face is extracted from the image data by the feature extraction unit, the generation unit may include location information of the image data in the entity. The information processing apparatus according to claim 1, wherein the associated knowledge data is generated. The feature extraction unit converts the image data included in the page data into a feature amount expression,
The generation unit classifies the feature quantity expression converted by the feature extraction unit into a plurality of clusters, and associates with the entity the location information of image data corresponding to the feature quantity expression belonging to the cluster having the largest number of data. The information processing apparatus according to claim 1, wherein the knowledge data is generated by. The priority order determination unit increases the priority order as the number of the IP addresses extracted by the entity extraction unit increases.
The information processing apparatus according to any one of claims 1 to 8 . A collection unit that collects page data including image data and text data,
From the text data included in the page data, an entity extraction unit that extracts an entity related to the image data,
A priority order determination unit that determines the priority order of the IP addresses for which the collection unit collects data, based on the number of the entities for each IP address to which the image data belongs extracted by the entity extraction unit;
An information processing apparatus including. Computer
A collecting step for collecting page data including image data and text data,
An entity extraction step of extracting an entity related to the image data from the text data included in the page data,
A feature extraction step of extracting features from the image data included in the page data,
When a feature related to the entity is extracted in the feature extracting step, a generating step of generating knowledge data in which the location information of the image data is associated with the entity,
An information processing method for executing
Determining the priority order of the IP addresses for collecting the data in the collecting step based on the number of the entities for each IP address to which the image data belongs extracted in the entity extracting step.
Information processing method. Computer
A collecting step for collecting page data including image data and text data,
An entity extraction step of extracting an entity related to the image data from the text data included in the page data,
A priority order determining step of determining a priority order of IP addresses for collecting data in the collecting step, based on the number of the entities for each IP address to which the image data belongs extracted in the entity extracting step;
Information processing method for executing . Computer,
A collection unit that collects page data including image data and text data,
From the text data included in the page data, an entity extraction unit that extracts an entity related to the image data,
A feature extraction unit that extracts features from the image data included in the page data,
When a feature related to the entity is extracted by the feature extraction unit, a generation unit that generates knowledge data in which the location information of the image data is associated with the entity,
A priority order determination unit that determines the priority order of the IP addresses for which the collection unit collects data, based on the number of the entities for each IP address to which the image data belongs extracted by the entity extraction unit,
Program to function as. Computer,
A collection unit that collects page data including image data and text data,
From the text data included in the page data, an entity extraction unit that extracts an entity related to the image data,
A priority order determination unit that determines the priority order of the IP addresses for which the collection unit collects data, based on the number of the entities for each IP address to which the image data belongs extracted by the entity extraction unit,
Program to function as.

Images